MemoryVault

Give your AI a memory that is yours. A local-first personal knowledge base + MCP server over your own notes and documents. Your AI finally knows what you know — and none of it leaves your machine.

本地优先的 AI 个人记忆层：把你的笔记和文档变成 Claude / Cursor / ChatGPT 都能调用的个人知识库，数据不出本机。

Why

Every AI chat starts from zero. You re-paste the same background, re-explain the same decisions, and your notes sit in folders that no model can see. MemoryVault fixes that:

• Local-first — indexing and search run on your machine. Works fully offline.

• Zero-config — no API key needed to start. Keyword search works out of the box; add embeddings later for semantic search.

• Model-neutral — one vault, usable from Claude Desktop, Cursor, ChatGPT, or any MCP client.

• Your data, exportable — everything lives in a single SQLite file. Walk away anytime.

Related MCP server: loci-mcp

Quickstart

Requires Node.js 20+.

# from a clone of this repo (npm package name TBD before launch)
git clone https://github.com/wang2025-ai/memoryvault.git
cd memoryvault
npm install && npm run build

# 1. initialize the vault (~/.memoryvault)
node bin/mvault.js init

# 2. index your notes (recursively: .md/.txt/.json/.pdf/.html)
node bin/mvault.js index ~/notes

# 3. keep folders in sync as you edit (optional)
node bin/mvault.js watch ~/notes

# 4. search it from the terminal
node bin/mvault.js search "rate limiting"

# 5. expose it to your AI via MCP
node bin/mvault.js serve

Connect your AI

Claude Desktop / Cursor — add to claude_desktop_config.json (or .cursor/mcp.json):

{
  "mcpServers": {
    "memoryvault": {
      "command": "node",
      "args": ["/absolute/path/to/memoryvault/bin/mvault.js", "serve"]
    }
  }
}

Claude Code:

claude mcp add memoryvault -- node /absolute/path/to/memoryvault/bin/mvault.js serve

Restart your client. You can now ask things like "what do my notes say about rate limiting?" — the AI calls memory_search against your vault.

Semantic search (optional)

Without configuration, MemoryVault uses SQLite FTS5 trigram + BM25 keyword search — fast, offline, and surprisingly good for exact phrases (English and Chinese).

For semantic ("meaning-based") search, add an embedding provider to ~/.memoryvault/config.json:

{ "embedding": { "provider": "ollama", "model": "nomic-embed-text" } }

or

{ "embedding": { "provider": "openai" } }

Then run mvault reembed (or just index again). Queries and results are fused with Reciprocal Rank Fusion across both layers. If the embedding provider is down, search degrades to keyword mode automatically — it never breaks.

Ollama keeps embeddings fully local; OpenAI sends chunk text to the API (your call).

CLI reference

Every search result cites its origin as path:line, so the AI (and you) can jump straight to the source. All commands support --json for scripting.

Landing page

site/index.html is a self-contained, dependency-free landing page (hero, how-it-works, MCP config copy button, waitlist form). Deploy it to Cloudflare Pages / Netlify / GitHub Pages as-is, then set WAITLIST_ENDPOINT in the file to your Formspree endpoint or Pages Function to collect signups.

Custom vault location: set the MEMORYVAULT_HOME environment variable.

MCP tools

Privacy

• Notes, chunks, FTS index and vectors all live in one SQLite file (~/.memoryvault/vault.db).

• Nothing phones home. No telemetry. No accounts.

• Delete the file, and everything is gone — it's your data.

Roadmap

• mvault watch — incremental indexing of folders

• PDF / HTML ingestion, line-number citation anchors

• Vault Cloud sync (paid) — multi-device, encrypted

• Browser extension: "save to memory"

• Shared team vaults

Development

npm install
npm run dev -- search "test"   # run CLI from source
npm test                       # vitest
npm run lint                   # typecheck
npm run build                  # compile to dist/

中文简介

MemoryVault 让你的 AI 拥有"只属于你的记忆"：一行配置把本地笔记接入 Claude/Cursor/ChatGPT（通过 MCP），检索默认离线可用（FTS5 三元组 + BM25），配置嵌入模型后升级为语义检索并自动融合，嵌入失败自动降级。所有数据存于单个 SQLite 文件，随时可迁移。详见上方英文文档。

License

MIT